MétaCan
Menu
← Back to cohort

Rate and Time Course of Thromboembolism Events in Cancer Patients

2011· article· en· W2522728205 on OpenAlexaffabout
Russell D. Hull, Tazmin Merali, Allan Mills, Jane Liang, Nelly Komari

Bibliographic record

VenueBlood · 2011
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsTrillium Health CentreSanofi (Canada)University of Calgary
Fundersnot available
KeywordsMedicineCancerPopulationMedical recordIncidence (geometry)Emergency medicineEmergency departmentVenous thromboembolismCancer registryPediatricsIntensive care medicineInternal medicineThrombosis

Abstract

fetched live from OpenAlex

Abstract Abstract 4773 Background: Venous thromboembolism (VTE) prophylaxis has been recommended in clinical guidelines as an appropriate strategy for hospitalized cancer patients based on evidence of reduced VTE events and reduced mortality. However, current guidelines do not specify the appropriate length of VTE prophylaxis in this population. Real world data is needed to understand the prevalence of symptomatic and confirmed VTE for this patient population to help clinicians determine strategies regarding the appropriate duration of treatment. Objective: To document the incidence of symptomatic late VTE events in hospitalized patients who have active cancer. Methods: Charts from 1134 consecutive medical patients age > 60 years who were hospitalized in the Calgary region and discharged between January and February 2008 were abstracted using standardized case record forms. All hospitals in the region use a common unique patient identifier number, thus enabling the tracking of subsequent patient visits to the emergency room, inpatient admissions or outpatient visits occurring anywhere in the region's acute care system. Any identified patient was followed for a subsequent visit related to VTE. Active cancer patients were defined as those who have a cancer diagnosis at hospital admission and have a planned cancer surgery or receiving cancer treatment or were receiving palliative treatment or whose cancer treatment was not specified. Records were excluded if the patient was admitted for VTE or to rule out VTE, receiving chronic anticoagulation, experiencing acute coronary syndromes, had a hospital stay ≤ 3 days, had a remote cancer history, was a surgical or orthopedic patient, or pregnant. Data was collected on the timing of VTE related events for up to 100 days post discharge. Results: A total of 358 patients met criteria over the review period. Seventy-three percent (261/358) of all active cancer patients received mechanical or pharmacological prophylaxis in hospital. Twenty-three percent of these patients were identified as requiring medical care for symptoms associated with VTE. Confirmation of VTE by diagnostic testing occurred in 4.8% (95% CI, 2.6% to 7%) while the other 18% (95% CI, 14.0% to 22%) had diagnostic tests that were negative or inconclusive. The mean length of time to confirmed first VTE event was 38.2 days post admission. Conclusion: This study demonstrates that in a real life setting 23% of active cancer patients would develop symptoms of VTE requiring a health professional's attention with 4.8% having VTE confirmed by diagnostic testing. These events occurred despite thromboprophylaxis in hospital and suggest that the risk of symptomatic VTE could be higher in real life compared to that reported in randomized clinical trials where patients are screened for asymptomatic VTE. These findings show that the prevalence of VTE warrants consideration of extended thromboprophylaxis in active cancer patients, as the benefits of extended prophylactic therapy may outweigh the risks in this population. Disclosures: Hull: sanofi-aventis: Consultancy; LEO Pharma: Consultancy; Pfizer: Consultancy; Portola: Consultancy; Merck: Consultancy; Bayer: Consultancy; Johnson & Johnson: Consultancy. Merali:sanofi-aventis: Consultancy; Amgen: Consultancy; Pfizer: Consultancy; BMS: Consultancy; Abbott: Consultancy; Boehringer Ingelheim: Consultancy; Genzyme: Consultancy; LEO Pharma: Consultancy; Nycomed: Consultancy; Otsuka: Consultancy. Mills:Pfizer: Consultancy; sanofi-aventis: Research Funding. Komari:sanofi-aventis: Employment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.260
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicVenous Thromboembolism Diagnosis and Management→French-language works237,207→